Exemplo n.º 1
0
 def load_svm(self, filename=None):
   if not filename:
     svm_file = config.get_classifier_filename(self, self.cls, self.train_dataset)
   else:
     svm_file = filename
   if not os.path.exists(svm_file):
     print("Svm %s is not trained"%svm_file)
     return None
   else:  
     model = load_svm(svm_file)
     return model
Exemplo n.º 2
0
 def train(self, pos, neg, kernel, C):
   y = [1]*pos.shape[0] + [-1]*neg.shape[0]
   x = np.concatenate((pos,neg))
   model = train_svm(x, y, kernel, C)
   self.svm = model
   print 'model.score(C=%d): %f'%(C, model.score(x,y))
   table_t = svm_proba(x, model)
   y2 = np.array(y)
   y2 = (y2+1)/2 # switch to 0/1
   ap,_,_ = Evaluation.compute_cls_pr(table_t[:,1], y2)
   print 'ap on train set: %f'%ap
   filename = config.get_classifier_filename(self, self.cls, self.train_dataset)
   self.svm = model
   self.save_svm(model, filename)
   return model